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There is no safe universal my.cnf for every 1GB or 2GB Ubuntu server. The reliable approach is to budget memory for Ubuntu and co-resident services first, give InnoDB a deliberately conservative buffer pool, and cap connection-driven allocations. A high max_connections value can exhaust RAM even when the buffer pool looks reasonable.
The profile below is a measured starting point, not a drop-in prescription. Begin with the example values, record real memory and workload behavior, then change one group of settings at a time. On a shared low-memory VPS, preserving headroom and avoiding swap is more important than maximizing cache size.
What a low-RAM MySQL configuration has to protect
MySQL shares the host with the Linux kernel, filesystem cache, SSH, web servers, PHP or application workers, monitoring agents, and sometimes background jobs. The memory left for MySQL is therefore less than the machine’s advertised RAM. A configuration that is reasonable on a dedicated database host can trigger swapping or an out-of-memory kill on a small web VPS.
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- Ubuntu and the kernel: the operating system needs working memory and benefits from filesystem cache.
- Other services: web and PHP workers can consume more RAM during traffic spikes than they do when idle.
- MySQL global allocations: the InnoDB buffer pool, data dictionaries, performance instrumentation, caches, and other server-wide structures.
- Connection-dependent allocations: thread stacks and working buffers that can be multiplied by simultaneous sessions.
The practical consequence is simple: set the buffer pool only after reserving room for the first two budgets, and set max_connections from observed application concurrency rather than from a large template.
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A conservative starting profile
This example keeps the major per-thread buffers small, limits internal temporary tables, and uses a modest table cache. Every value marked as an example must be recalculated for the actual host.
[mysqld] # Reserve memory for Ubuntu, SSH, web/PHP workers, and filesystem cache. innodb_buffer_pool_size = 256M # example only; recalculate for the host # Keep concurrency aligned with the application pool. max_connections = 25 # example only; do not use blindly # Keep per-thread working buffers modest on a small host. sort_buffer_size = 256K read_buffer_size = 128K read_rnd_buffer_size = 256K join_buffer_size = 256K # Keep internal temporary tables from consuming unbounded RAM. tmp_table_size = 16M max_heap_table_size = 16M # Increase only while file-descriptor headroom remains. table_open_cache = 512
The 256M pool, 25-connection ceiling, 16M temporary-table limits, and buffer values above are illustrative starting values. They are not universal defaults. A host running a large web stack may need a smaller pool; a quiet database-only host may eventually support a larger one after measurement.
How large should innodb_buffer_pool_size be?
innodb_buffer_pool_size is the main cache for InnoDB table data and indexes. A larger pool can reduce disk reads when the working set fits, but it also removes memory that Ubuntu and connection handling may need. On a shared 1GB or 2GB machine, avoiding swap is usually a better first objective than filling a high percentage of RAM.
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Use the official percentage as an upper reference, not a target
Oracle’s MySQL documentation says a typical recommendation is 50 to 75 percent of system memory. That range is primarily useful as a reference for a database host with few competing services. It is not a promise that 75 percent is safe on an Ubuntu VPS running a web server and workers.
The same documentation says the default configuration is designed to let MySQL start on a virtual machine with approximately 512MB of RAM. In the cited MySQL 8.0 reference, the default buffer pool is 134217728 bytes, or 128MB, and the documented minimum is 5242880 bytes. Defaults and minimums can vary by release, so verify the values on the installed version before treating them as limits.
Budget from the host downward
- Measure total RAM and current resident memory while the application is handling representative traffic.
- Reserve enough for Ubuntu, filesystem cache, SSH, the web server, PHP or application workers, and normal spikes.
- Reserve room for MySQL’s non-buffer-pool allocations and the maximum realistic concurrent connections.
- Assign the remainder to the buffer pool, then observe swap and latency before increasing it.
| Host situation | Practical starting decision | What is established |
|---|---|---|
| 1GB Ubuntu host shared with web or PHP workers | A 256M buffer pool is a conservative example; reduce it if the non-MySQL workload already uses substantial RAM. | The 256M figure is an example configuration, not a documented universal recommendation. |
| 2GB Ubuntu host shared with application services | Start from measured free headroom rather than automatically doubling the pool. Increase only after observing stable resident memory and no swap growth. | No universal 2GB value is stated by Oracle or Ubuntu documentation. |
| Dedicated database host | The 50–75% system-memory range is an upper reference, subject to workload, version, and other MySQL allocations. | Oracle MySQL documents this typical range for system memory. |
MySQL can resize the InnoDB buffer pool dynamically in supported versions. Dynamic resizing does not remove the need for testing: a larger pool can expose pressure elsewhere, and a smaller pool can increase disk I/O and query latency.
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Why max_connections can kill a small server
max_connections is a ceiling, not a reservation that is fully allocated at startup. It is still a latent risk because each active session can require thread-stack space and working buffers. A sudden burst of requests can therefore consume much more RAM than an idle process appears to use.
Set the ceiling from the application’s real pool size and traffic pattern. If an application pool opens 10 database sessions, a ceiling of 25 may provide controlled room for administrative and burst connections. Setting the value to hundreds merely to avoid connection errors can make an out-of-memory event more likely.
The documented planning formula
Oracle’s InnoDB startup guidance gives a memory estimate that includes:
innodb_buffer_pool_size + key_buffer_size + max_connections * (sort_buffer_size + read_buffer_size + binlog_cache_size) + max_connections * 2MB
The manual warns that this formula must be adapted to the MySQL version and configuration. It is a planning aid, not a complete process-memory model: Ubuntu, the filesystem cache, table metadata, performance features, temporary tables, and application services still need headroom.
Keep per-thread buffers modest
The example uses sort_buffer_size 256K, read_buffer_size 128K, read_rnd_buffer_size 256K, and join_buffer_size 256K. These buffers are useful for particular operations, but raising them globally to fix one slow query multiplies the worst-case memory requirement across concurrent sessions.
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If one query needs a larger work area, investigate its execution plan, indexes, and access pattern first. A global increase can improve that query while making every concurrent request more dangerous. Change these variables only with a measured reason and recheck memory under concurrency.
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Temporary tables: control RAM without hiding bad queries
tmp_table_size and max_heap_table_size limit the size of internal temporary tables that can remain in memory. Keep the two values equal so that one setting does not silently permit a larger in-memory table than the other. The example sets both to 16M, a workload-dependent starting point for a small host.
Small limits can force larger temporary results to disk. That may increase latency, but it is often safer than allowing several concurrent sessions to allocate large in-memory tables. After changing the limits, inspect temporary-table status counters and query latency. A rising disk-spill rate points to queries that may need better indexes or a different plan; simply raising both limits can trade disk I/O for RAM exhaustion.
There is no single low-RAM value established for every workload. Reporting, large sorts, joins, and aggregation-heavy applications can have very different temporary-table behavior from a simple transactional site.
Table-open cache and file descriptors
table_open_cache keeps table handles available so MySQL does not repeatedly open and close them. A larger cache can help when status counters show table-open churn, but every open table consumes file descriptors. Ubuntu guidance recommends increasing this cache gradually and checking the service’s descriptor limit.
The example value of 512 is deliberately moderate. Raise it only when:
- status counters indicate frequent table opens or cache misses;
- the operating-system and service file-descriptor limits have clear headroom;
- resident memory remains stable under the application’s peak table usage.
If descriptor limits are tight, increasing the cache can create failures unrelated to RAM. Treat the cache and descriptor ceiling as one capacity decision rather than tuning the MySQL variable in isolation.
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Settings that should usually remain at their defaults
innodb_buffer_pool_instances
The MySQL reference associates multiple buffer-pool instances with pools of at least 1GB and with scalability on larger pools. For a small pool, leave the default or use one instance unless measurements show a specific contention problem. Splitting a small pool into many instances adds complexity without creating more memory.
innodb_dedicated_server
Do not enable innodb_dedicated_server automatically on a shared VPS. MySQL documents this option for an instance on a dedicated server that can use all available system resources. Explicit low-RAM values are safer when Ubuntu, a web server, workers, or monitoring agents share the machine.
Apply the configuration safely on Ubuntu
- Identify the active configuration. Ubuntu package layouts can differ by release, and MySQL may read more than one configuration fragment. Confirm which server configuration is active before editing.
- Back up the file or fragment. Keep a copy of the working configuration so a failed change can be reverted quickly.
- Record a baseline. Note resident memory, swap use, active connections, query latency, and temporary-table counters during representative traffic.
- Add one coherent group. Set the buffer pool, connection ceiling, per-thread buffers, temporary-table limits, and table cache together only when you can observe their combined effect. Avoid unrelated changes in the same maintenance window.
- Check syntax and startup behavior. A typo or unsupported variable can prevent MySQL from restarting. Review the service log immediately if startup fails, then restore the backup or remove the offending line.
- Restart only when required. Some variables are dynamic and can be changed online; others require a restart. Follow the behavior documented for the exact MySQL release installed on the host.
- Observe a representative period. Include peak application traffic, scheduled jobs, backups, and administrative activity. An idle test cannot reveal connection bursts or temporary-table pressure.
Keep a change log with the old value, new value, timestamp, workload conditions, and measured result. This prevents repeatedly applying a setting that helped one short-lived symptom but harmed the server later.
Validation checklist after every change
- Resident memory: confirm that MySQL and the whole host retain safety headroom at peak load.
- Swap: check both current swap use and swap-in or swap-out activity. Growing swap activity is a warning even when the process has not yet been killed.
- Connections: compare peak active sessions with
max_connections. A ceiling far above observed demand is unused risk. - Latency and throughput: look for slower queries, request timeouts, and lower throughput after memory changes.
- Temporary tables: check whether in-memory and disk-based temporary-table counts changed as expected.
- File descriptors: verify that table-cache changes do not push the service toward its descriptor limit.
- Errors and kills: inspect MySQL and system logs for allocation failures, aborted connections, restart loops, or an operating-system OOM kill.
Change one setting or one tightly related group at a time. If memory pressure appears, reduce connection and per-thread limits first, then reconsider the buffer pool. Increasing the pool while the host is swapping usually makes the underlying problem worse.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Diagnosis by symptom
MySQL is killed by the operating system
First reduce the maximum realistic concurrency and the per-thread buffers, then lower the buffer pool if non-MySQL services are also under pressure. Check whether a traffic burst, worker-process increase, or scheduled job coincided with the kill. Do not respond by raising max_connections.
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The server is swapping after increasing max_connections
Undo the connection increase and compare peak active sessions with the application’s pool. Restore modest sort, read, random-read, and join buffers. A large ceiling multiplies these allocations only when sessions are active, so the problem may appear suddenly under load rather than at restart.
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Queries became slower after reducing memory limits
Determine whether the slowdown comes from extra disk-based temporary tables, a smaller hot-data cache, or connection queueing. Examine query plans and indexes before globally raising a buffer. If the working set genuinely needs more cache, increase the pool only after confirming that Ubuntu and co-resident services have stable headroom.
Table-open errors appear after raising table_open_cache
Check the operating-system and service file-descriptor ceilings, then lower the cache if headroom is insufficient. Increase it gradually only when table-open churn is demonstrated by status counters.
Choosing a profile for 1GB versus 2GB
| Decision axis | 1GB shared VPS | 2GB shared VPS |
|---|---|---|
| Buffer-pool strategy | Favor a small explicit pool such as the 256M example, then reduce it if other services need the memory. | Use measured remaining memory; a larger total does not justify doubling the pool without observing workload and swap behavior. |
| Connection ceiling | Keep it close to the application’s real pool and burst requirement; 25 is only an example. | Still derive it from observed concurrency. Extra RAM is not a reason to permit unbounded sessions. |
| Per-thread buffers | Keep the example modest values unless a measured query requires a targeted change. | Use the same conservative approach; concurrency can consume the additional RAM quickly. |
| Temporary tables | Start with equal 16M limits and inspect disk spills. | Keep limits workload-dependent and increase only when spill costs are measured and memory headroom is real. |
| Table cache | Use a moderate value such as 512 only with descriptor headroom. | Raise gradually only when table-open churn is demonstrated. |
The table is a decision framework, not two fixed files. Two 2GB servers can need radically different settings if one runs many application workers and the other is nearly dedicated to MySQL.
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Stop tuning when larger caches no longer improve latency, when swap remains active, or when the application routinely reaches the connection ceiling. At that point, improve query and application behavior, reduce unnecessary worker concurrency, move services off the host, add RAM, or scale the database environment. Ubuntu guidance explicitly points to application improvements and more capable hardware when configuration changes reach diminishing returns.
Low-RAM tuning is successful when the host remains responsive during its real peak workload, MySQL avoids out-of-memory kills and sustained swapping, and latency is predictable. A smaller cache with stable headroom is a better production configuration than a larger cache that wins an idle benchmark and fails during traffic spikes.
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